Relationship of peripartum inflammation with reproductive health in dairy cows
Bibliographic record
Abstract
Failure of a robust but well-regulated immune response may result in reproductive tract inflammatory disease, such as metritis, purulent vaginal discharge, or endometritis. Metritis is consistently associated with reduced diversity of the uterine microbiome. Similarly, purulent vaginal discharge at 4 to 6 wk postpartum is strongly associated with bacterial infection of the uterus. Conversely, the microbiome of healthy cows and those with subclinical endometritis is generally similar, so endometritis is thought to be a consequence of dysregulation of inflammation rather than changes in uterine microbiota. There is an emerging concept that inflammation is not only a reaction to injury or disease but that it can be a consequence of or precursor to metabolic disturbances. The degree of systemic inflammation is associated with the level of trauma and bacterial contamination of the uterus or mammary gland, the degree of fat mobilization and release of nonesterified fatty acids, and perhaps leaky gut, all of which result in the release of proinflammatory cytokines. Therefore, uterine inflammation may be exacerbated by systemic inflammation, but may also contribute to heightened systemic inflammation in transition cows. However, clarity and progress are limited by a lack of validated criteria to quantify systemic inflammation and to identify its sources.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".